File size: 6,929 Bytes
f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca 309b641 f86b482 f2de1ca af60005 f86b482 af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca af60005 f2de1ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 |
import os
import re
import random
import numpy as np
# !!! spaces must be imported before torch/CUDA
import spaces
from huggingface_hub import login
from diffusers import DiffusionPipeline
import gradio as gr
import torch
from utils import QPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
login(token=os.environ["HF_TOKEN"])
model_repo_id = os.environ["MODEL_ID"]
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
pipe = QPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)
MAX_SEED = 65535
MAX_IMAGE_SIZE = 128
@spaces.GPU # Enable ZeroGPU if needed
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
num_inference_steps=10,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
[prompt],
batch_size=1,
generator=generator,
num_inference_steps=num_inference_steps
).images[0]
return image, seed
examples = [
"Structure: (LR 文 英). Style: style001",
"Structure: (TL 广 東). Style: style028",
"Structure: (TB 艹 (LR 禾 魚)). Style: style015",
"Structure: (TB 敬 音). Style: style013",
"Structure: (LR 釒 馬). Style: style018",
"Structure: (BL 走 羽). Style: style022",
"Structure: (LR 羊 大). Style: style005",
"Structure: (LR 鹿 孚). Style: style017",
"Structure: (OI 口 也). Style: style002",
]
# Map style images to style names (use real image files later)
style_options = {
"images/style001.png": "style001",
"images/style002.png": "style002",
"images/style003.png": "style003",
"images/style004.png": "style004",
"images/style005.png": "style005",
"images/style006.png": "style006",
"images/style007.png": "style007",
"images/style008.png": "style008",
"images/style009.png": "style009",
"images/style010.png": "style010",
"images/style011.png": "style011",
"images/style012.png": "style012",
"images/style013.png": "style013",
"images/style014.png": "style014",
"images/style015.png": "style015",
# "images/style016.png": "style016", very similar to 002
"images/style017.png": "style017",
"images/style018.png": "style018",
"images/style019.png": "style019",
"images/style020.png": "style020",
"images/style021.png": "style021",
"images/style022.png": "style022",
"images/style023.png": "style023",
"images/style024.png": "style024",
"images/style025.png": "style025",
"images/style026.png": "style026",
"images/style027.png": "style027",
"images/style028.png": "style028",
"images/style029.png": "style029",
}
def apply_style_on_click(evt: gr.SelectData, prompt_text):
index = evt.index
style_label = list(style_options.values())[index]
if re.search(r"Style: [^\n]+", prompt_text):
return re.sub(r"Style: [^\n]+", f"Style: {style_label}", prompt_text)
else:
return prompt_text.strip() + f" Style: {style_label}"
# CSS for fixing Gallery layout
css = """
#col-container {
margin: 0 auto;
max-width: 800px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # NeoChar ")
gr.Markdown(""" - Generate New Chineses Characters (Hanzi/Kanji)
- Combine components in a creative way
- Write them in style
- A Gen-AI's implementation of [Lin Yutang's Ming-Kwai Typewriter](https://thereader.mitpress.mit.edu/the-uncanny-keyboard/)
- [README](https://huggingface.co/spaces/lqume/neochar/blob/main/README.md) for more""")
gr.Markdown(" ## QuickStart: select an example, edit components, pick a style, then 'generate'")
gr.HTML("""
<style>
.gallery-container .gallery-item {
width: 60px !important;
height: 60px !important;
padding: 0 !important;
margin: 4px !important;
border-radius: 4px;
overflow: hidden;
background: none !important;
box-shadow: none !important;
}
.gallery-container .gallery-item img {
width: 64px !important;
height: 64px !important;
object-fit: cover;
display: block;
margin: auto;
}
.gallery-container button {
all: unset !important;
padding: 0 !important;
margin: 0 !important;
border: none !important;
background: none !important;
box-shadow: none !important;
}
.gallery__modal,
.gallery-container .preview,
.gallery-container .gallery-item:focus-visible {
display: none !important;
pointer-events: none !important;
}
</style>
""")
gallery = gr.Gallery(
value=list(style_options.keys()),
label="Click any image",
columns=7,
allow_preview=False,
height=None,
elem_classes=["gallery-container"]
)
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Generate", scale=0, variant="primary")
gallery.select(
fn=apply_style_on_click,
inputs=[prompt],
outputs=prompt
)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=20,
step=1,
value=10,
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
|